# Can a Model Learn to Generate Better Augmented Data?

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**Category:** 🤗Transformers\
**Created:** [August 20, 2025, 3:43pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fcan-a-model-learn-to-generate-better-augmented-data%2F167444 "2025-08-20T15:43:34Z")\
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**Author:** ![ZAID041](https://avatars.discourse-cdn.com/v4/letter/z/ecae2f/32.png) [@ZAID041](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FZAID041)%5C
**Post date:** [August 20, 2025, 3:43pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fcan-a-model-learn-to-generate-better-augmented-data%2F167444%2F1 "2025-08-20T15:43:34Z")

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While working on a competition recently, I noticed something interesting: my model would **overfit really quickly**. With only ~2k rows, it was clear the dataset wasn’t enough. I wanted to try standard augmentation techniques, but I also felt that using **LLMs** could be the best way to improve things… though most require **API keys** , which makes experimenting a bit harder.

That got me thinking: why don’t we have a dedicated model built for **text augmentation** yet? We have so many types of models, but no one has really made a **“super” augmentation model** that generates high-quality data for downstream tasks.

Here’s the approach I’m imagining—turning a language model into a **self-teaching augmentation engine** :

- **Start small, think big** – Begin with a lightweight LM, like **Qwen3-0.6B** , so it’s fast and easy to experiment with.
- **Generate new ideas** – Give it prompts to create **augmented versions of your text** , producing more data than your original tiny dataset.
- **Keep only the good stuff** – Use a **strong multi-class classifier** to check each new example. If it preserves the original label, keep it; if not, discard it.
- **Learn from success** – Fine-tune your LM on the filtered examples, so it **improves its augmentation skills** over time.
- **Repeat and grow** – Run the loop again with fresh data, gradually building a **self-improving, super-augmentation model** that keeps getting smarter and generates high-quality data for any downstream task.

The main challenge is **filtering correctly**. I think a classifier with **100+ classes** could do the job: if the label stays the same, keep it; if not, discard it.

I haven’t started working on this yet, but I’m really curious to hear your thoughts: could something like this make augmentation easier and more effective, or are **classic techniques** already doing the job well enough? Any feedback, ideas, or experiences would be **amazing**!